Systems and Methods For Predictive  Analytics for Site Initiation and Patient Enrollment

ABSTRACT

Methods and systems for predictive analytics for site initiation and patient enrollment are disclosed. One method may include: receiving a user&#39;s selection of one or more parameters associated with a clinical trial; accessing a database of data associated with a plurality of previous clinical trials; comparing the one or more parameters to the previous clinical trials; determining one or more factors associated with the clinical trial based on the comparison; and displaying the parameters and the factors on a display.

CROSS REFERENCE TO RELATED APPLICATIONS

This application claims priority to U.S. Provisional Application No. 61/663,292, filed on Jun. 22, 2012, entitled “Method and System to Manipulate Multiple Selections Against a Population of Elements;” U.S. Provisional Application No. 61/663,057, filed on Jun. 22, 2012, entitled “Systems and Methods For Predictive Analytics For Site Initiation and Patient Enrollment;” U.S. Provisional Application No. 61/663,299, filed on Jun. 22, 2012, entitled “Methods and Systems for Predictive Clinical Planning and Design and Integrated Execution Services;” U.S. Provisional Application No. 61/663,398, filed on Jun. 22, 2012, entitled “Systems and Methods for Subject Identification (ID) Modeling;” U.S. Provisional Application No. 61/663,219, filed Jun. 22, 2012, entitled “Systems and Methods for Analytics on Viable Patient Populations;” U.S. Provisional Application No. 61/663,357, filed Jun. 22, 2012; entitled “Methods and Systems for a Clinical Trial Development Platform;” U.S. Provisional Application No. 61/663,216, filed Jun. 22, 2012; entitled “Systems and Methods for Data Visualization.” The entirety of all of which is hereby incorporated by reference herein.

FIELD OF THE INVENTION

The present invention relates generally to systems and methods for the creation and analysis of clinical trials. The present invention relates more specifically to systems and methods for predictive analytics for site initiation and patient enrollment.

BACKGROUND

Clinical trials for molecules that may become pharmaceutical products often last for years. The core cost of the trial is affected primarily by the length of the trial. And a delay of even a single day can cost hundreds or thousands and even millions of dollars.

One of the variables most likely to affect the length of the trial has historically been patient enrollment, i.e., how quickly investigators are able to bring patients into a trial. This is due in part to the fact that a typical trial has a very limited amount of start time at the beginning and analysis time at the end. Further, the trial will require a fixed duration of treatment time in order to provide sufficient data for a submission. Because of these factors, the trials cannot be arbitrarily shortened. Further, a trial requires a certain minimum number of patients in order to prove efficacy and safety of a molecule. Thus, patient enrollment becomes the primary variable factor.

Since the amount of time that it takes to complete patient enrollment is critical to the length of a trial, this aspect is often the focus of companies' efforts to shorten the trials. Various companies have attempted to address the need to shorten the trial. These companies include drug companies, software companies, and consulting groups.

The conventional methods that these companies have implemented for shortening the length of a trial have focused on statistical models. A company might, for example, look at a hundred prior trials and attempt to extrapolate what would happen in a new trial based on the data culled from the earlier trial. Such models provide a relatively static view of the potential trial. Further, they often do not provide details regarding the impact of variables that go into creating the trial, such as the impact of conducting the trial in particular countries and at particular investigation sites. Thus the conventional systems lack flexibility and provide data that may or may not accurately reflect the planned trial.

SUMMARY

Embodiments of the present disclosure provide systems and methods for Predictive Analytics for Site Initiation and Patient Enrollment. In one embodiment, a three-tier system provides a client application to view and set parameters related to planning a clinical trial, a patient modeling engine for determining likely outcomes based on the parameters supplied by the user, and a database for storing historical data related to previously-completed and ongoing trials. The application in such an embodiment receives the user's parameter selections and attempts to determine in which countries and at which investigation sites the clinical trial should be conducted to meet the user's goals. The application is able to provide the user with the impact of the user's selections on the amount of time the trial is likely to take and the likely cost of the trial. The user may also be provided with best and worst case scenarios so that the user can balance risks and costs associated with a planned clinical trial.

In one embodiment, the user is also provided with a clinical plan perspective—a view that encompasses many different molecules and clinical trials for those molecules. Such a view allows the user to determine if all the various planned clinical trials can be performed simultaneously. And if not, i.e., if saturation exits, the application allows the user to vary parameters so that the user can prioritize which trials should be done first.

This embodiment is mentioned not to limit or define the invention, but to provide an example of an embodiment of the invention to aid understanding thereof. Embodiments are discussed in the Detailed Description, and further description of the invention is provided there. Advantages offered by the various embodiments of the present invention may be further understood by examining this specification.

BRIEF DESCRIPTION OF THE FIGURES

These and other features, aspects, and advantages of the present disclosure are better understood when the following Detailed Description is read with reference to the accompanying drawings, wherein:

FIG. 1 is a block diagram illustrating an exemplary environment for implementation of one embodiment of the present disclosure;

FIG. 2 is a screen shot of a country editor view, showing color-coded countries and a popup categories table;

FIG. 3 is a screen shot of an investigator site editor view in one such embodiment;

FIG. 4 is a flow chart illustrating the general steps involved in one such process;

FIG. 5 is a flowchart illustrating one method for performing tuning on an enrollment model according to one embodiment of the present disclosure;

FIG. 6 is a flowchart illustrating method of adjusting enrollment for geographic distribution in one embodiment of the present disclosure;

FIG. 7 is a screen shot of an enrollment country view in one embodiment of the present disclosure;

FIG. 8 is a screenshot of a graph, illustrating the risk associated with various scenarios in one embodiment of the present disclosure;

FIG. 9 is a screen shot of enrollment views in embodiments of the present disclosure;

FIG. 10 is a screen shot of enrollment views in embodiments of the present disclosure; and

FIG. 11 is a screen shot of enrollment views in embodiments of the present disclosure.

DETAILED DESCRIPTION

Embodiments of the present disclosure provide systems and methods for predictive analytics for site initiation and patient enrollment.

Illustrative Embodiment of the Present Disclosure

One illustrative embodiment of the present disclosure comprises an application for selecting sites and analyzing patient enrollment for clinical trials. The embodiment allows a user to access an application that presents a variety of clinical trial-related parameters for various countries and investigators. These parameters may include, for example, the population of a country, the regulatory environment, and the level of risk associated with conducting a trial in a particular country.

Once the user has set the parameters that are of most interest for the countries, the user is presented with a graphical representation of the countries that reflects the appropriateness of a particular country in view of the parameters specified by the user. For example, the countries may be color-coded. The user is then able to drill down into particular countries to identify particular investigators.

In the illustrative embodiment, for each investigator, the user is again able to specify investigator-specific parameters, such as the number of trials that an investigator has run, the time it typically takes an investigator to enroll patients and other relevant parameters. Once the user selects these parameters, the user may then be presented with a graphical representation of the investigators or investigator sites, illustrating the relative value of using particular site for a particular set of parameters.

The process is iterative; the user is able to change the parameters for countries and investigators to determine the most appropriate sites to utilize for a clinical trial. The results of the user's selections can then be used as part of a larger clinical trial analysis application.

This illustrative embodiment neither limits nor defines the disclosure. Rather, the illustrative embodiment is meant to provide an example of how the present disclosure may be implemented.

Illustrative Environment

Referring now to the drawings, in which like numerals indicate like elements throughout the several figures, FIG. 1 is a block diagram illustrating an exemplary environment for implementation of one embodiment of the present disclosure. The embodiment shown in FIG. 1 includes a client 100 that allows a user to interface with an application server 200, web server 300, and/or database 400 via a network 500.

The client 100 may be, for example, a personal computer (PC), such as a laptop or desktop computer, which includes a processor and a computer-readable media. The client 100 also includes user input devices, such as a keyboard and mouse or touch screen, and one or more output devices, such as a display. In some embodiments of the disclosure, the user of client 100 accesses an application or applications specific to one embodiment of the disclosure. In other embodiments, the user accesses a standard application, such as a web browser on client 100, to access applications running on a server such as application server 200, web server 300, or database 400. For example, in one embodiment, the memory of client 100 stores applications including a design studio application for planning and designing clinical trials. The client 100 may also be referred to as a terminal in some embodiments of the present disclosure.

Such applications may be resident in any suitable computer-readable medium and executable on any suitable processor. Such processors may comprise, for example, a microprocessor, an ASIC, a state machine, or other processor, and can be any of a number of computer processors, such as processors from Intel Corporation, Advanced Micro Devices Incorporated, and Motorola Corporation. The computer-readable media stores instructions that, when executed by the processor, cause the processor to perform the steps described herein.

The client 100 provides a software layer, which is the interface through which the user interacts with the system by receiving and displaying data to and from the user. In one embodiment, the software layer is implemented in the programming language C# (also referred to as C Sharp). In other embodiments, the software layer can be implemented in other languages such as Java or C++. The software layer may be graphical in nature, using visual representations of data to communicate said data to one or more users. The visual representations of data may also be used to receive additional data from one or more users. In one embodiment, the visual representation appears as a spider-like layout of nodes and connectors extending from each node to a central node.

Embodiments of computer-readable media comprise, but are not limited to, an electronic, optical, magnetic, or other storage device, transmission device, or other device that comprises some type of storage and that is capable of providing a processor with computer-readable instructions. Other examples of suitable media comprise, but are not limited to, a floppy disk, CD-ROM, DVD, magnetic disk, memory chip, ROM, RAM, PROM, EPROM, EEPROM, an ASIC, a configured processor, all optical media, all magnetic tape or other magnetic media, or any other medium from which a computer processor can read instructions. Also, various other forms of computer-readable media may be embedded in devices that may transmit or carry instructions to a computer, including a router, private or public network, or other transmission device or channel, both wired and wireless. The instructions may comprise code from any suitable computer-programming language, including, for example, C, C++, C#, Visual Basic, Java, Python, Perl, and JavaScript.

The application server 200 also comprises a processor and a memory. The application server may execute business logic or other shared processes. The application server may be, for example, a Microsoft Windows Server operating in a .NET framework, an IBM Weblogic server, or a Java Enterprise Edition (J2E) server. While the application server 200 is shown as a single server, the application server 200, and the other servers 300, 400 shown may be combined or may include multiple servers operating together to perform various processes. In such embodiments, techniques such as clustering or high availability clustering may be used. Benefits to architectures such as these include redundancy and performance, among others.

In the embodiment shown in FIG. 1, the application server 200 is in communication with a web server 300 via a network connection 250. The web server 300 also comprises a processor and a memory. In the memory are stored applications including web server software. Examples of web server software include Microsoft Internet Information Services (IIS), Apache Web Server, and Sun Java System Web Server from Oracle, among others.

In the embodiment shown in FIG. 1, the web server 300 is in communication with a database 400 via a network connection 350 and a network connection 450. The web server 300 provides a web service layer that, together or separate from application server 200, acts as middleware between a database 400 and the software layer, represented by the client 100. The web server 300 communicates with the database 400 to send and retrieve data to and from the database 400.

The network 500 may be any of a number of public or private networks, including, for example, the Internet, a local area network (“LAN”), or a wide area network (“WAN”). The network connections 150, 250, 350, and 450 may be wired or wireless networks and may use any known protocol or standard, including TCP/IP, UDP, multicast, 802.11b, 802.11g, 802.11n, or any other known protocol or standard. Further, the network 100 may represent a single network or different networks. As would be clear to one of skill in the art, the client 100, servers 200, 300, and database 400 may be in communication with each other over the network or directly with one another.

The database 400 may be one or a plurality of databases that store electronically encoded information comprising the data required to plan, design, and execute a clinical trial. In one embodiment, the data comprises one or more design elements corresponding to the various elements related to one or more clinical trials. The database 400 may be implemented as any known database, including a SQL database or an object database. Further, the database software may be any known database software, such as Microsoft SQL Server, Oracle Database, MySQL, Sybase, or others.

Country Editor

One embodiment of the present disclosure comprises a country editor for selecting the countries for the application to consider in determining the sites in which to conduct a clinical trial. The country editor allows the user to identify, based on historical information, which countries have investigator sites likely to enroll the necessary patients in a timely manner. The country editor in one such embodiment presents choices to the user in the form of slider input tools (“sliders”). The user then adjusts the sliders to set limits for data categories associated with each slider. For example, limits may comprise minimum, ideal levels, and maximum levels. In one embodiment, the data categories include:

-   -   Patient Prevalence (per 100 k population);     -   Extrapolated Prevalence (# of patients);     -   Total Population;     -   Trial Saturation (# of active trials);     -   Regulatory Approval (cycle time in days);     -   Cycle Time (CT) Materials (cycle time in days);     -   Historical Recruitment (patients per site per month (PSM));     -   Clinical Research Associates (RA's) (total #); and     -   Site Start-Up (cycle time in days).

The application executing on the application server 200 executes an algorithm to compare the limits of a category to a category value obtained from data stored in the database 400 for each country. The application then generates a value for each category and country combination and generates a view of that data for presentation on the client 100. The embodiments described herein may operate in a similar way within this architecture or may be implemented in other ways. For example, in some embodiments, the client 100 may perform much of the processing in addition to providing the display and receiving the user's input.

FIG. 2 is a screen shot 200 of a country editor view, showing color-coded countries and a popup categories table 202. The countries and categories are assigned colors based on the slider values and the proprietary data. In the embodiment shown, upon selection of a country, a popup table 202 presents the categories and category colors for the selected country. The category and country values and/or colors may be saved as a country plan for use by the enrollment editor or by other elements of a larger clinical trial system.

In the embodiment shown in FIG. 2, the sliders 204 divide the data range of each category into three sub-ranges. Data falling within the first sub-range is assigned the number 0, data in the middle sub-range is assigned the number 1, and data in the last sub-range is assigned the number 2. Categories are colored according to their assigned values, where 0, 1 and 2 correspond, respectively, to the colors red, yellow and green. In the embodiment shown, a user may change the category colors by changing the limits on the sliders.

In one embodiment, the values for all the categories for a particular country are averaged to determine a value for the country. In such an embodiment, the country may, for example, be colored red if the value falls below 0.8, yellow if the value falls between 0.8-1.2, and green if the value exceeds 1.2. A country can also be colored to indicate that the country cannot or should not be selected. Indicators such as shape, cross-hatching, symbols or other methods may be used instead of or addition to the colors shown.

Investigator Site Editor

One embodiment of the present disclosure comprises a site editor for selecting the investigation sites for the application to consider in determining the sites in which to conduct a clinical trial. Preferably, the investigator site editor is functionally similar to the country editor and relies on the results of selections made in the country editor and/or other parts of the system. FIG. 3 is a screen shot of an investigator site editor view 300 in one such embodiment. The investigator site editor view 300 shown comprises color-coded circles 302 representing the likelihood and timing of enrolling patients within geographical boundaries, such as regions, based on proprietary investigator data. Once displayed, the user can select other geographical groupings for displaying grouped data such as countries, states or cities. As shown in FIG. 3, circles 302 can be colored red, yellow, green, and partially one color and partially another. In the embodiment shown, the data categories include:

-   -   Trials in Therapeutic Area (# of trials);     -   Site Start-Up (SSU) (cycle time in days);     -   Patient Randomization (average #/trial);     -   Patients Screened (average #/trial);     -   Screen Failure Rate (% of screen patients);     -   Drop Out Rate (% of randomized patients);     -   Queries (#/100 pages); and     -   Performance Index (average across trials).

The embodiment shown uses the three colors, red, green, and yellow as indicia of the acceptability or applicability of investigators. However, while the various countries and investigators are described in terms of the color coding, other indicia may be used to identify the various described categories.

In the embodiment shown in FIG. 3, the yellow and green-coded Investigators make up the available population. The following data rules are followed for determining the measures set out above:

-   -   If the Investigator SSU is null or 0, then the Country SSU is         used;     -   If the Investigator SSU StDev is null, then use the Country SSU         StDev;     -   If the Investigator PSM is null or 0, then the Country PSM is         used;     -   If the Investigator PSM StDev is null, then use the Country PSM         StDev;

Once the population of potential payments is established, then if it is less than needed, the user will expand the selection. If the population of potential patients is greater than is needed, the user may narrow the population by making various selections, including, for example, selecting an option called “Sites like These.” Running the model yields a potentially narrower population of “Modeled” selections.

Illustrative Enrollment Process

In embodiments of the present disclosure, the user is attempting to determine how most successfully to enroll patients in a clinical trial given the relative importance of the various parameters of the trial. One embodiment deals with high level metrics related to the country, investigators and patients. These high level metrics drive the time it will take to enroll patients and the amount of money it will cost for the modeled patients.

In such an embodiment, the application tracks the number of countries that are available for the trial, the number of countries in the set created based on the values of the parameters supplied by the user, and the number of countries selected (i.e., predicted) by the model. The application tracks these same three metrics (available, selected, and modeled) for the investigators.

Based on these metrics and on historical data, the application is able to determine the potential number of patients. Then, based on the number of desired patients to be enrolled in the study, the application can calculate the predicted number of patients to enroll. Based on these predictions, the model in such an embodiment can determine the modeled (i.e., predicted) time to enroll the desired number of patients as well as the cost of the modeled patients. A user can then use this information for planning purposes. Embodiments of the present disclosure utilize an iterative process or enrollment modeling.

FIG. 4 is a flow chart 400 illustrating the general steps involved in one such process. In the process shown, the user begins enrollment modeling. As a first step, the investigator uses various views, such as those illustrated in FIGS. 2 and 3 to model patients 402. The user then adjusts the model based on geographic distribution 404, time 406, and the total number of investigators used 408. For example, the user may adjust for high-performing to low-performing sites based on cost even though that could increase the risk. In some embodiments, the user is able to use a feature called a “quick rule” that automatically selects sites with, for example, the lowest startup time or with a startup time that is lower than a number of days selected by the user. FIG. 4 is merely illustrative. Various other types of adjustments may be made in embodiments of the present disclosure.

FIG. 5 is a flowchart illustrating one method for performing tuning on an enrollment model according to one embodiment of the present disclosure. In the embodiment shown, the application determines the number of modeled patients. The user then determines if the number of modeled patients is greater than or equal to the number of desired patients 502. If so, the required number of patients has been identified, and the process ends. If not, the process continues in an iterative fashion until a sufficient number of patients have been modeled.

Next, the application determines whether the number of potential patients is greater than or equal to the number of desired patients 504. If so, then the time limit may be increased to increase the number of modeled patients 506. This may be done manually or automatically. In the embodiment shown, the time limit is increased to 36 months.

If the number of potential patients is less than the desired number of patients, the application highlights the unelected investigators, if any. If unselected investigators exist 508, the user is provided with a list of those investigators to select from 510, and the application again evaluates whether the potential number of patients is greater than the desired number 504.

If no unselected investigators exist, the application determines whether any “red” investigators exist 512. These are investigators that the application has identified as not satisfying the criteria selected by the user initially. If these investigators exist, the user is provided with an opportunity to revise the enrollment criteria to be more inclusive 514, and the application again evaluates whether the potential number of patients is greater than the desired number 504.

In the embodiments shown, if no investigators are indicated as “red” investigators in the model, then the application determines that all investigators have been exhausted 516. The application provides the user with the capability of adjusting parameters or adding custom sites, i.e., sites that do not meet the criteria but might be appropriate for the trial in any event. The application again evaluates whether the potential number of patients is greater than the desired number and repeats the remaining evaluations until the number of modeled patients meets or exceeds the number of desired patients.

FIG. 6 is a flowchart illustrating a method of adjusting enrollment for geographic distribution 600 according to one embodiment of the present disclosure. In the process shown, the user reviews the country summary tab of a view provided by the application 602. For example, in one embodiment, the user might utilize the view shown in FIG. 2; in another embodiment, the user might utilize the view shown in FIG. 7, which is described below.

The user determines if the countries that the user desires to be included are included 604. If so the application determines if the country's modeled (i.e., predicted) number of patients meets the desired number 606. If so, the process shown ends. If the desired countries are not listed, the user selects investigators in the additional desired countries 608, and the application displays the updated list of countries.

If the countries' modeled patients do not meet the desired number, the user can use a Required Patients section to set minimum and/or maximum number of patients by country 610. If the number of patients is now meeting the user's goals 612, the user can continue the process for additional countries. If not, the user returns to the process illustrated in FIG. 6

FIG. 7 is a screen shot of an enrollment country view 700 in one embodiment of the present disclosure. This view provides the user with the distribution of sites 702, patients 704 and costs 706 across countries. Such a view facilitates the tuning process described herein and highlights risk (a.k.a. buffer) for particular trials.

In the embodiment shown, the left hand column lists the countries selected out of those available—of 89 countries, 9 were selected, 7 contributed patients (non-red highlight). The view also illustrates the number of available investigators—of 1274 investigators, 46 were selected, and 34 contributed patients. The view also illustrates the potential patients and modeled patients—of 539 potential patients (Historical Max), 201 patients were modeled/predicted. In this case, the desired number of patients is 200. The view also provides the user with the predicted time for the trial—8 months.

The risk or buffer is determined by comparing the available number of countries or investigators to the number selected and the number that are eventually modeled. The larger the difference, the lower the risk. If the modeled number is very nearly the same as the number selected, then a higher likelihood exists that the modeled plan will not be successfully implemented. The patient buffer is determined by comparing the number of potential patients to the number of modeled patients.

FIG. 8 is a screenshot of a graph 800, illustrating the risk associated with various scenarios in one embodiment of the present disclosure. In the embodiment shown, the best 804, median 806, and worst 808 scenarios are shown to highlight buffer or risk in the model. Time is shown on the x-axis, and the number of patients is illustrated on the y-axis. The number of patients desired is 200, which is illustrated by the solid line 802 from left to right across the graph. In this example, all the illustrated scenarios meet the desired goal for patients, but in the worst case scenario 808 (the farthest right line), time is significantly impacted.

FIGS. 9, 10, and 11 are screen shots of enrollment views in embodiments of the present disclosure. These screenshots illustrate how data may be brought together and displayed to the user in a way that allows the user to vary the parameters of the clinical trial to balance the risk of various scenarios with the goals of the trial.

Advantages

Embodiments of the present disclosure provide many advantages over conventional methods of predicting the enrollment for clinical trials. For example, embodiments of the present disclosure allow the enrollment process to be wholly data driven. The performance data from previous trials is collected and organized for use in the model. The team is then able to set expectations for the types of investigators best-suited to conduct the trial. For example, investigators with many patients can often start up very quickly.

Once the types of investigators are chosen, embodiments of the present disclosure are able to take a mathematical approach to analyzing and presenting data regarding the actual investigators and investigation sites. The embodiments can then create graphical representations, e.g., line graphs that display information, such as predictions for likely scenarios based on average performance as well as best and worst-case scenarios based on outlier data.

Conventional systems are statistical in nature; they use real-world data but treat all data as the same. In other words, these conventional systems use a pool of performance data that includes all of the studies ever done to create an average. For example, the system may run data through a model 100 times to derive variability. This is both less accurate and can take many minutes or even hours to execute. In contrast, embodiments of the present disclosure build the models dynamically. They are able to illustrate the effect of a particular variable or set of variables on a planned clinical trial and present that data in real-time.

Further, embodiments of the present disclosure provide a user with the ability to understand the variability inherent in planning clinical trials. For example, if a user is contemplating using physical investigation sites in China versus the U.S., the risks and costs associated with each of those countries is illustrated to the user so that the user can make an informed decision regarding the risks. Some factors that drive variability and which are available to the user in various embodiments include: the patient community, investigators, countries

General

The foregoing description of the embodiments of the disclosure has been presented only for the purpose of illustration and description and is not intended to be exhaustive or to limit the invention to the precise forms disclosed. Numerous modifications and adaptations are apparent to those skilled in the art without departing from the spirit and scope of the invention. 

What is claimed:
 1. A method for predictive analytics, the method comprising: receiving a user's selection of one or more parameters associated with a clinical trial; accessing a database of data associated with a plurality of previous clinical trials; comparing the one or more parameters to the previous clinical trials; determining one or more factors associated with the clinical trial based on the comparison; and displaying the parameters and the factors on a display.
 2. The method of claim 1, wherein receiving the user's selection of one or more parameters comprises determining that the user has adjusted one or more sliders associated with the parameters.
 3. The method of claim 1, wherein the factors comprises one or more of: an estimated cost of the clinical trial, an estimated required number of patients, and a predicted length of time of the clinical trial.
 4. The method of claim 1, wherein the previous clinical trials are associated with the clinical trial.
 5. The method of claim 1, wherein at least one of the one or more parameters comprises a country in which to perform the clinical trial.
 6. The method of claim 5, wherein the one or more parameters further comprises one or more of: the country's population, a prevalence of patients in the country, the country's regulatory environment, a number of clinical trials performed in the country, a length of time of the clinical trial, a length of time to start the clinical trial, historical data associated with the country, or a level of risk associated with conducting the clinical trial in the country.
 7. The method of claim 1, further comprising: receiving the user's selection of one or more new parameters associated with the clinical trial; comparing the one or more new parameters to the previous clinical trials; determining one or more additional factors associated with the clinical trial based on the comparison; and updating the display to show the one or more additional factors.
 8. The method of claim 7, wherein the new parameters comprise one or more of: a number of clinical trials in a therapeutic area, a length of time to start a clinical trial, investigator specific parameters, or data associated with previous clinical trials.
 9. The method of claim 8, wherein the investigator specific parameters comprise one or more of: a number of trials that an investigator has run or a time it typically takes the investigator to enroll patients.
 10. The method of claim 8, wherein the data associated with previous clinical trials comprises one or more of: a number of patients, a number of failures, a dropout rate, or a screen failure rate.
 11. A non-transitory computer readable medium comprising program code, which when executed is configured to cause a processor to: receive a user's selection of one or more parameters associated with a clinical trial; access a database of data associated with a plurality of previous clinical trials; compare the one or more parameters to the previous clinical trials; determine one or more factors associated with the clinical trial based on the comparison; and display the parameters and the factors on a display.
 12. The non-transitory computer readable medium of claim 11, wherein receiving the user's selection of one or more parameters comprises determining that the user has adjusted one or more sliders associated with the parameters.
 13. The non-transitory computer readable medium of claim 11, wherein the previous clinical trials are associated with the clinical trial.
 14. The non-transitory computer readable medium of claim 11, wherein the factors comprise one or more of: an estimated cost of the clinical trial, an estimated required number of patients, and a predicted length of time of the clinical trial.
 15. The non-transitory computer readable medium of claim 11, wherein at least one of the one or more parameters comprises a country in which to perform the clinical trial.
 16. The non-transitory computer readable medium of claim 15, wherein the one or more parameters further comprises one or more of: the country's population, a prevalence of patients in the country, the country's regulatory environment, a number of clinical trials performed in the country, a length of time of the clinical trial, a length of time to start the clinical trial, historical data associated with the country, or a level of risk associated with conducting the clinical trial in the country.
 17. The non-transitory computer readable medium of claim 11, further comprising program code, which when executed by the processor is configured to cause the processor to: receive the user's selection of one or more new parameters associated with the clinical trial; comparing the one or more new parameters to the previous clinical trials; determining one or more additional factors associated with the clinical trial based on the comparison; and updating the display to show the one or more additional factors.
 18. The non-transitory computer readable medium of claim 17, wherein the new parameters comprise one or more of: a number of clinical trials in a therapeutic area, a length of time to start a clinical trial, investigator specific parameters, or data associated with previous clinical trials.
 19. The non-transitory computer readable medium of claim 18, wherein the investigator specific parameters comprise one or more of: a number of trials that an investigator has run or a time it typically takes the investigator to enroll patients.
 20. The non-transitory computer readable medium of claim 18, wherein the data associated with previous clinical trials comprises one or more of: a number of patients, a number of failures, a dropout rate, or a screen failure rate. 